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Books > Computing & IT > General theory of computing
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Learning with Kernels - Support Vector Machines, Regularization, Optimization, and Beyond (Paperback)
Loot Price: R2,234
Discovery Miles 22 340
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Learning with Kernels - Support Vector Machines, Regularization, Optimization, and Beyond (Paperback)
Series: Adaptive Computation and Machine Learning series
Expected to ship within 10 - 15 working days
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A comprehensive introduction to Support Vector Machines and related
kernel methods. In the 1990s, a new type of learning algorithm was
developed, based on results from statistical learning theory: the
Support Vector Machine (SVM). This gave rise to a new class of
theoretically elegant learning machines that use a central concept
of SVMs--kernels-for a number of learning tasks. Kernel machines
provide a modular framework that can be adapted to different tasks
and domains by the choice of the kernel function and the base
algorithm. They are replacing neural networks in a variety of
fields, including engineering, information retrieval, and
bioinformatics. Learning with Kernels provides an introduction to
SVMs and related kernel methods. Although the book begins with the
basics, it also includes the latest research. It provides all of
the concepts necessary to enable a reader equipped with some basic
mathematical knowledge to enter the world of machine learning using
theoretically well-founded yet easy-to-use kernel algorithms and to
understand and apply the powerful algorithms that have been
developed over the last few years.
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